|
|
Attribute Reductions of Fuzzy-Crisp Concept Lattices Based on Matrix |
LIN Yidong 1,2, LI Jinjin1, ZHANG Chengling1 |
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000; 2. School of Mathematical Sciences, Xiamen University, Xiamen 361005 |
|
|
Abstract A matrix representation of fuzzy-crisp formal concepts based on fuzzy formal contexts and a matrix approach of attribute reduction are studied. Firstly, the matrix representations of the extension and intension of fuzzy-crisp concept are developed from the matrix perspective, respectively. The definition and the computing method of attribute granular matrix are formulated subsequently. To find the minimal generation group of fuzzy-crisp concept lattice, matrix judgment theorem of meet-irreducible elements is discussed, and it is utilized to construct the attribute reduction framework preserving the extents of meet-irreducible elements. The significance measure of attribute is proposed by introducing the similarity degree between attribute subsets with aforementioned matrices. And then a heuristic matrix-method of attribute reduction is developed. Finally, numerical experiments verify the effectiveness of the proposed approach.
|
Received: 23 May 2019
|
|
Fund:Supported by National Natural Science Foundation of China(No.11871259,11701258,61379021,61603173), Natural Science Foundation of Fujian Province(No.2019J01748) |
Corresponding Authors:
LI Jinjin, Ph.D., professor. His research interests include topology, rough set and concept lattices.
|
About author:: LIN Yidong, Ph.D. candidate. His research interests include rough set and concept lattices.ZHANG Chengling, master student. Her research interests include concept lattices. |
|
|
|
[1] WILLE R. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts // RIVAL I, ed. Ordered Sets. Berlin, Germany: Springer, 1982: 445-470. [2] CARPINETO C, ROMANO G. A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval. Machine Learning, 1996, 24(2): 95-122. [3] HUANG C C, LI J H, MEI C L, et al. Three-Way Concept Lear-ning Based on Cognitive Operators: An Information Fusion Viewpoint. International Journal of Approximate Reasoning, 2017, 83: 218-242. [4] RAJAPAKSE R K, DENHAM M. Text Retrieval with More Realistic Concept Matching and Reinforcement Learning. Information Proce-ssing and Management, 2006, 42(5): 1260-1275. [5] VALTCHEV P, MISSAOUI R, GODIN R. Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges // Proc of the International Conference on Formal Concept Analysis. Berlin, Germany: Springer, 2004: 352-371. [6] XU W H, LI W T. Granular Computing Approach to Two-Way Lear-ning Based on Formal Concept Analysis in Fuzzy Datasets. IEEE Transactions on Cybernetics, 2016, 46(2): 366-379. [7] SHAO M W, YANG H Z, WU W Z. Knowledge Reduction in Formal Fuzzy Contexts. Knowledge-Based Systems, 2015, 73: 265-275. [8] SHAO M W, LEUNG Y, WANG X Z, et al. Granular Reducts of Formal Fuzzy Contexts. Knowledge-Based Systems, 2016, 114: 156-166. [9] LI K W, SHAO M W, WU W Z. A Data Reduction Method in Formal Fuzzy Contexts. International Journal of Machine Learning and Cybernetics, 2017, 8(4): 1145-1155. [10] MAO H, MIAO H R. Attribute Reduction Based on Directed Gra-ph in Formal Fuzzy Contexts. Journal of Intelligent and Fuzzy Systems, 2018, 34(6): 4139-4148. [11] WU X Y, WANG J Y, SHI L, et al. A Fuzzy Formal Concept Analysis-Based Approach to Uncovering Spatial Hierarchies among Vague Places Extracted from User-Generated Data. International Journal of Geographical Information Science, 2019, 33(5): 991-1016. [12] FORMICA A. Similarity Reasoning in Formal Concept Analysis: From One-to Many-Valued Contexts. Knowledge and Information Systems, 2019, 60(2): 715-739. [13] KRAJCˇI S. Cluster Based Efficient Generation of Fuzzy Concepts. Neural Network World, 2003, 13(5): 521-530. [14] YAHIA S B B, AROUR K, SLIMANI A, et al. Discovery of Compact Rules in Relational Databases. Information Science Journal, 2000, 4(3): 497-511. [15] GANTER B, WILLE R. Formal Concept Analysis. Berlin, Germany: Springer, 1999. [16] ZHANG W X, WEI L, QI J J. Attribute Reduction in Concept La-ttice Based on Discernibility Matrix // Proc of the International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. Berlin, Germany: Springer, 2005: 157-165. [17] SHI L L, YANG H L. Object Granular Reduction of Fuzzy Formal Contexts. Journal of Intelligent and Fuzzy Systems, 2018, 34(1): 633-644. [18] LI L F. Multi-level Interval-Valued Fuzzy Concept Lattices and Their Attribute Reduction. International Journal of Machine Lear-ning and Cybernetics, 2017, 8(1): 45-56. [19] 张清新.基于布尔矩阵的概念格属性约简方法.硕士学位论文.漳州:漳州师范学院, 2012. (ZHANG Q X. Attribute Reduction Method for Concept Lattices Based on Boolean Matrices. Zhangzhou, China: Zhangzhou Normal University, 2012.) [20] 张呈玲,李进金,林艺东.不协调决策形式背景的矩阵型属性约简[J/OL]. [2019-04-15]. http://kns.cnki.net/kcms/detail/11.5602.TP.20190612.1553.018.html. (ZHANG C L, LI J J, LIN Y D. Matrix-Type Attribute Reduction for Inconsistent Formal Decision Contexts[J/OL]. [2019-04-15]. http://kns.cnki.net/kcms/detail/11.5602.TP.20190612.1553.018.html.) [21] LIN Y D, LI J J. A Boolean Matrix Approach for Granular Reduction in Formal Fuzzy Contexts. Journal of Intelligent and Fuzzy Systems, 2019, 37(4): 5217-5228. [22] LIN Y D, LI J J, TAN A H, et al. Granular Matrix-Based Know-ledge Reductions of Formal Fuzzy Contexts [C/OL]. [2019-04- 15]. https://xs.scihub.ltd/https:doi.org/10.1007/s13042-019-01022-4. [23] LI J H, MEI C L, LÜ Y J. A Heuristic Knowledge-Reduction Met-hod for Decision Formal Contexts. Computers and Mathematics with Applications, 2011, 61(4): 1096-1106. |
|
|
|